Proceedings ArticleDOI
Human action recognition in video by 'meaningful' poses
Snehasis Mukherjee,Sujoy Kumar Biswas,Dipti Prasad Mukherjee +2 more
- pp 9-16
TLDR
A graph theoretic technique for recognizing actions at a distance by modeling the visual senses associated with human poses, and the results clearly demonstrate the superiority of the approach over the present state-of-the-art.Abstract:
We propose a graph theoretic technique for recognizing actions at a distance by modeling the visual senses associated with human poses. Identifying the intended meaning of poses is a challenging task because of their variability and such variations in poses lead to visual sense ambiguity. Our methodology follows a bag-of-words approach. Here "word" refers to the pose descriptor of the human figure corresponding to a single video frame and a "document" corresponds to the entire video of a particular action. From a large vocabulary of poses we prune out ambiguous poses and extract 'meaningful' [6] poses - for each action type in a supervised fashion - using centrality measure of graph connectivity [16]. The number of 'meaningful' poses per action is determined by setting a bound on the centrality measure. We evaluate our methodology on four standard activity recognition datasets and the results clearly demonstrate the superiority of our approach over the present state-of-the-art.read more
Citations
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Proceedings ArticleDOI
Temporal key poses for human action recognition
TL;DR: This approach integrates the advantages of human action recognition in static images using action key poses and motion based approaches using the variants of Motion History Images (MHI) and Motion Energy Images (MEI) to extract a new representation of temporal key poses.
Dataset
Solving the confusion of body sides problem in two-dimensional human pose estimation
TL;DR: I would also like to recognize SAFRAN-France (MORPHO) for sponsoring a fundamental part of this work under the Research Award Program.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Proceedings Article
An iterative image registration technique with an application to stereo vision
Bruce D. Lucas,Takeo Kanade +1 more
TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.
Book
Pattern Recognition and Machine Learning (Information Science and Statistics)
TL;DR: Looking for competent reading resources?